Wireless communication data analysis and reporting

ABSTRACT

A communication analytics engine that executes in conjunction with a data collection platform may provide a unified and scalable solution for call data aggregation and processing. A data collection platform may establish a communication connection with a wireless carrier network. The data collection platform may collect call data of multiple user devices via the communication connection, in which the multiple user devices may use the wireless carrier network to initiate and receive calls to one or more additional devices. The data collection platform may convert the call data into a format that is readable by the communication analytics engine. The communication analytics engine may analyze the call data to generate analytic results that includes one or more key performance indicators (KPIs).

CROSS REFERENCE TO RELATED PATENT APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/216,956, filed on Sep. 10, 2015, entitled “Big Data MachineLearning Technologies for Analyzing Call Data,” which is herebyincorporated by reference in its entirety.

BACKGROUND

Voice over LTE (VoLTE) is the use Long-Term Evolution (LTE) wirelesscommunication infrastructure to provide voice communication services.Since VoLTE is a relatively new technology, many wireless communicationcarriers have encountered limitations in analyzing and finding the rootcauses of problems experience by VoLTE customers. The problems may beissues such as call drops, network congestion and access failures, lowmean opinion score (MOS) regarding call quality, etc. Such issues mayresult in shortened calls and repeated calls between same numbers withina short period of time. VoLTE performance issues may be difficult totroubleshoot using legacy network analysis methodologies that aredeveloped for use with third generation (3G) mobile communicationtechnology. As a result, the customer care operations of wirelesscommunication carriers may be unable to properly resolve customercomplaints regarding VoLTE services.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures, in which the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items.

FIG. 1 illustrates an example architecture for performing wirelesscommunication data analysis and reporting.

FIG. 2 is a block diagram showing various components of a communicationanalytics engine for performing wireless communication data analysis andreporting.

FIG. 3 is an illustrative user interface that provides key performanceindicators information and other call related analytic information fordisplay in response to user request inputs.

FIG. 4 is a flow diagram of an example process for implementing acommunication analytics engine to collect and analyze call data of awireless carrier network.

FIG. 5 is a flow diagram of an example process for detecting issues withrespect to wireless communication and generating alerts regarding theissues.

FIG. 6 is a flow diagram of an example process for providing views ofcall analytics data in accordance with user requests.

DETAILED DESCRIPTION

This disclosure is directed to techniques for analyzing call data todetect issues with wireless communication services that are provided bya wireless carrier network, and providing alerts regarding the detectedissues. A communication analytics engine may receive call data frommonitoring application on multiple user devices. The user devices mayinclude smart phones, phoneblets, phablets, and/or so forth. In variousembodiments, the communication analytics engine may be a customanalytics application that executes on top of a data aggregation andprocessing platform. The communication analytics engine may generate keyperformance indicators (KPIs) from the call data based on KPIconfiguration settings. Additionally, the communication analytics enginemay generate alerts according to predefined alert rules. For example, aseries of KPIs that are generated by the communication analytics engineover time may trigger an alert that an abnormal condition is developingwith respect to some VoLTE-related infrastructure components of awireless carrier network. In another example, an alert rule may generatean alert to a customer who is experiencing poor call performance at homethat Wi-Fi calling is not enabled. In other instances, the KPIs that arecalculated may provide information about calls at a subscriber level, inwhich the information may be related to call durations, call drops, callaccess failures, one-way audio situations, and/or so forth.

The implementation of the communication analytics engine to execute inconjunction with the data aggregation and processing platform mayprovide a unified and scalable solution for call data aggregation andprocessing. The data and aggregation and processing platform may enablethe use of custom analytics engines to provide KPIs to a wirelesscarrier network for different corporate sectors, such as networkdevelopment, operations, and customer care. The KPIs that are providedby the communication analytics engine from the call data may beotherwise overly burdensome or virtually impossible to manually obtainin a timely manner due to the large volume of call data that typicallyinundate a wireless carrier network. Further, the techniques may providean advanced approach to customer care with proactive and individualizedtreatments of wireless subscribers in real time. The techniquesdescribed herein may be implemented in a number of ways. Exampleimplementations are provided below with reference to the following FIGS.1-5.

Example Architecture

FIG. 1 illustrates an example architecture for performing wirelesscommunication data analysis and reporting. The architecture 100 mayinclude a wireless carrier network 102 that is operated by a wirelesscommunication carrier or a service provider. The wireless carriernetwork 102 may provide a wide range of mobile communication services,as well as ancillary services and features, to subscribers andassociated mobile device users. In various embodiments, the wirelesscarrier network 102 may provide wireless communication between userdevices 104(1)-104(N). Further, the wireless carrier network 102 mayalso provide communications that involves the use of VoLTE between theuser device 104(1)-104(N) and user devices that are external of thewireless carrier network 102. The user devices 104(1)-104(N) may includemobile handsets, smart phones, tablet computers, personal digitalassistants (PDAs), smart watches, and/or electronic devices.

The wireless carrier network 102 may be implemented using multipleinterconnected networks. In various embodiments, the wireless carriernetwork 102 may include multiple Radio Access Networks (RANs), such asthe RAN 106. The RANs may be connected to each other via regional groundnetworks. In turn, the regional ground networks may be connected to acore network 108 by a wide area network (WAN). Each regional portion ofthe wireless carrier network 102, such as those serving the user devices104(1)-104(N) may include one or more RANs and a regional circuit and/orpacket switched network and associated signaling network facilities.

The radio access network 106 may include a number of base stations, suchas the base stations 110(1)-110(N). In some embodiments, the basestations 110(1)-110(N) may be in the form of eNodeB nodes. Each eNodeBnode may include a base transceiver system (BTS) that communicates viaan antennae system over an air-link with one or more user devices thatare within range. The antenna system of an eNodeB node may includemultiple antennae that are mounted on a radio tower to provide acoverage area. The BTS may send radio communication signals to userdevices and receive radio communication signals from user devices. Theradio access networks may carry the user communications for the userdevices 104(1)-104(N) between the respective base stations 110(1)-110(N)and the core network 108.

The core network 108 may connect to a public packet data communicationnetwork, such as the Internet 112. Packet communications via the radioaccess network 106, the core network 108, and the Internet 112 maysupport a variety of services through the wireless carrier network 102.The Evolved Packet Core (EPC) of the radio access network 106 may useEvolved Packet System (EPS) bearer channels to route IP traffic from agateway in the radio access network 106 to a user device. A bearerchannel is an IP packet flow with a defined quality of service (QoS)between the Packet Data Network (PDN) Gateway (PGW) and the user device.The eNodeB nodes may be interconnected with each other by interfaces.The communication between eNodeB nodes may include Radio ResourceManagement (RRM) control data, which may manage multiple functions ofthe eNodeB nodes. These functions may include radio bearer control,radio admission control, radio mobility control, scheduling, and dynamicallocations of resources to user devices in both uplink and downlink.

The user devices 104(1)-104(N) may be equipped with device applications,such as the respective device applications 114(1)-114(N). Each of thedevice applications 114(1)-114(N) may collect call data from acorresponding user device as wireless communication calls are initiatedat the user device or received at the user device. In variousembodiments, each device application may obtain the call data from acorresponding user device via an IP Multimedia Subsystem (IMS) stacktrace of the user device. The device applications 114(1)-114(N) may sendcollected call data 116 through the radio access network 106 to the corenetwork 108, such that a data collection platform 118 may receive thecall data 116 via a communication connection with the core network 108.For example, the communication connection may be the Internet 112, anintranet, or a combination of the Internet 112 and the intranet. Thecollected call data 116 may include VoLTE call data for VoLTE calls thatare handled by the user devices 104(1)-104(N). In other embodiments,each device application may also obtain call data 116 in the form ofnon-VoLTE call data, such as circuit-switch call data, Wi-Fi call data,and/or so forth, from a corresponding user device. In additionalembodiments, subscriber identity module (SIM) applets on the userdevices 104(1)-104(N) may alternatively or concurrently send handsetreports that include the call data 116 and/or network performance datato the core network 108. The call data collected by a device applicationor a SIM applet may include parameters, measurements, and/or metricsassociated with incoming and outgoing calls as the calls are handled bya corresponding user device.

The data collection platform 118 may be implemented by one or morecomputing devices 120. In some embodiments, the computing devices 120may be servers that are part of a computing cloud. The data collectionplatform 118 may include a cloud layer that controls hardware resources,and a data management layer that manages data processing and storage.The cloud layer may provide software utilities for managing computingand storage resources. In various embodiments, the cloud layer mayprovide a generic user interface for handling multiple underlyingstorage services (e.g., local servers, Amazon AWS, Digital Ocean, etc.)that stores the call data collected by the data collection platform 118.The cloud layer may also provide an integrated view of multiple serversand clusters from various providers, such as Hortonworks, Cloudera,MapR, etc.). Additionally, the cloud layer may provide monitoringutilities to oversee utilization of resources and alerts for managingdata storage or processing capacity. Accordingly, the cloud layer mayfacilitate the deployment, configuration, and activation of local andcloud servers, as well as facilitate the deployment, configuration, andactivation of applications and/or services.

The data management layer may include software utilities and databasesthat facilitate the acquisition, processing, storage, reporting, andanalysis of data from multiple data sources. In various embodiments, thedata management layer may provide an API that decouples backend datamanagement elements from data processing elements, such that the datamay be distributed and stored in different data stores. For example,these data stores may include Hadoop distributed File System (HDFS),Apache Spark, Apache HBase, and/or so forth. The API of the datamanagement layer may be used by custom analytic engines and/or otherthird party tools to access the data in the different data stores. Thedata management layer may further include multiple data adaptors thatare able to obtain multiple types of network performance data 122 fromthe wireless carrier network 102, such network performance data 122 mayinclude RAN Operation Support System (OSS) counters, Call Detail Records(CDRs), VoLTE call traces, Session Initiation Protocol (SIP) trace data,Real-Time Transport Protocol (RTP) Control Protocol (RTCP) trace data,and/or other data.

A communication analytics engine 124 may execute on the data collectionplatform 118 to analyze the call data 116 and/or the network performancedata 122 and provide call analysis reports and alerts. In variousembodiments, the communication analytics engine 124 may generate keyperformance indicators (KPIs) from the call data based on KPIconfiguration settings. Additionally, the communication analytics enginemay generate alerts according to predefined alert rules. For example, aseries of KPIs that are generated by the communication analytics engineover time may trigger an alert that an abnormal condition is developingwith respect to some VoLTE-related infrastructure components of thewireless carrier network 102. In another example, the communicationanalytics engine 124 may use an alert rule to alert a customer who isexperience poor VoLTE call performance at home that Wi-Fi calling is notenabled. In other instances, the KPIs that are calculated may provideinformation about VoLTE calls at a subscriber level, in which theinformation may be related to call durations, call drops, call accessfailures, one-way audio situations, and/or so forth.

Example Computing Device Components

FIG. 2 is a block diagram showing various components of thecommunication analytics engine 124 for performing wireless communicationdata analysis and reporting. The data collection platform 118 and thecommunication analytics engine 124 may reside on one or more computingdevices 120. The computing devices 120 may include a communicationinterface 202, one or more processors 204, memory 206, and hardware 208.The communication interface 202 may include wireless and/or wiredcommunication components that enable the one or more computing devices120 to transmit data to and receive data from other networked devices.The hardware 208 may include additional user interface, datacommunication, or data storage hardware. For example, the userinterfaces may include a data output device (e.g., visual display, audiospeakers), and one or more data input devices. The data input devicesmay include, but are not limited to, combinations of one or more ofkeypads, keyboards, mouse devices, touch screens that accept gestures,microphones, voice or speech recognition devices, and any other suitabledevices.

The memory 206 may be implemented using computer-readable media, such ascomputer storage media. Computer-readable media includes, at least, twotypes of computer-readable media, namely computer storage media andcommunications media. Computer storage media includes volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD), high-definition multimedia/data storage disks, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other non-transmissionmedium that can be used to store information for access by a computingdevice. In contrast, communication media may embody computer-readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave, or other transmissionmechanism.

The processors 204 and the memory 206 of the computing device 200 mayimplement the data collection platform 118 and the communicationanalytics engine 124. The data collection platform 118 may include anauthentication module 210, a data collection module 212, an encryptionmodule 214, a data processing module 216, a unified storage module 218,and an API management module 220. The unified storage module 218 may bea part of the cloud layer of the data collection platform 118, while theremaining modules may be parts of the data management layer of the datacollection platform 118. The modules may include routines, programinstructions, objects, and/or data structures that perform particulartasks or implement particular abstract data types. The authenticationmodule 210 may perform credential management and automate authenticationto provide secure access to applications and data that are stored ondifferent wireless carrier network. For example, the authenticationmodule 210 may authenticate the data collection platform 118 to thewireless carrier network 102 for the purpose of retrieving the call data116 from the wireless carrier network 102.

The data collection module 212 may include a workflow scheduler thatperiodically checks for and retrieves newly available data from datasources, such as the wireless carrier network 102. In turn, the wirelesscarrier network 102 may be responsible for retrieving the call data 116and other data from the device applications 114(1)-114(N) via data pushor data pull techniques. The workflow scheduler may handle theextraction and the handling of the data based on configurable policies.For example, a configurable policy may specify the source data location,frequency of data retrieval, handling procedures for late arrival data,data retention period, and data disposal following an expiration of thedata retention period. The handling procedures for the late arrival datamay specify a predetermined cutoff period during which any data arrivinglate may be incorporated with data that is retrieved on time forprocessing.

In various embodiments, the data collection module 212 may employ filetransfer protocol (FTP), Hypertext Transfer Protocol (HTTP) posts,direct network infrastructure element connection, and/or other datatransfer techniques to obtain call data 116 from the device applications114(1)-114(N) or data from the SIM applets on the user devices104(1)-104(N). The call data 116 may be in the form of input data files,i.e., JavaScript Object Notation (JSON) records, which are received fromthe device applications 114(1)-114(N) on the user devices 104(1)-104(N).In some embodiments, the data collection module 212 may perform dataintegrity tests to verify that the content of the received data filesare not corrupt. The data collection module 212 may also use dataadaptors to interface with the core network 108 to collect networkperformance data 122 of the wireless carrier network 102.

The encryption module 214 may encrypt and/or decrypt sensitive data toprovide additional data security. For example, the wireless carriernetwork 102 may encrypt the call data 116 via an asymmetric or symmetricencryption algorithm, and the encryption module 214 may decrypt the calldata 116 using the same encryption algorithm. The data processing module216 may implement adaptor-specific logics to decode the input dataformat into a metadata representation. Accordingly, the input data maybe fed into other modules for analysis and storage. In variousembodiments, the data processing module 216 may merge call data asembodied in the JSON records of a particular time period into anintegrated data file. Subsequently, the data processing module 216 mayinterpret the information in the integrated data file into a convertedformat that is readable to the communication analytics engine 124.

The unified storage module 218 may store data across multiple virtualdata storage clusters with redundancy, so that the data may be optimizedfor quick access. The stored data may include the input data files,i.e., the JSON records, the integrated data file that includes data inthe converted format, calculated data from the communication analyticsengine 124, and/or so forth. In various embodiments, the call datastored by the unified storage module 218 may include messages andconfiguration information of subscribers, in which the data areaggregated based on time, network infrastructure element, and/or userdevice model. The API management module 220 provides an API that may beaccessed by other applications. Accordingly, the API may be used by thecommunication analytics engine 124 as well as other third-partyapplication to access the data that received and stored by the datacollection platform 118.

The communication analytics engine 124 may include a data aggregationmodule 222, a KPI computation module 224, an issue detection module 226,and a user interface module 228. The modules may include routines,program instructions, objects, and/or data structures that performparticular tasks or implement particular abstract data types.

The data aggregation module 222 may receive integrated data files ofcall data 116 from the data collection platform 118, in which the calldata 116 may be in the converted format. In turn, the data aggregationmodule 222 may aggregate the received data into multiple data setsaccording to grouping parameters. The grouping parameters may includespecific time periods (e.g., hourly, daily, etc.), routing networkcomponents, user device vendor types, user device models, and/or soforth. In other embodiments, the group parameters may be used toaggregate the call data 116 into multiple datasets that correspond todifferent levels of a telecommunication network hierarchy. For example,the call data 116 may be aggregated into data sets that correspond to abase station level, a Type Allocation Code (TAC) level, a service arealevel, and a geographical market level. The data aggregation module 222may designate a data retention period for each dataset. Upon anexpiration of the data retention period, the data in the correspondingdataset may be purged by the data aggregation module 222.

The KPI computation module 224 may compute a set of KPIs for the calldata in each dataset based on a KPI configuration file. Thus, the KPIcomputation module 224 may be directed to compute new KPIs from the calldata in each dataset through the modifications of existing KPIconfiguration files or the addition of new KPI configuration files.Additionally, the KPI computation module 224 may also perform othertypes of analysis on the call data in each dataset. For example, the KPIcomputation module 224 may generate KPIs for other types of calling,such as circuit-switched calling or Wi-Fi calling, that are performed bythe user devices. In another example, the KPI computation module 224 mayalso perform analysis that compare the KPIs that are generated for theseother types of calls to KPIs that are generated for the calls.

The issue detection module 226 may parse the generated KPIs and othercollected data to automatically detect conditions based on conditiondetection rules and generate alerts based on alert rules. The detectedconditions may include present performance issues with a user device ora network infrastructure element. The detected conditions may furtherinclude anomalies, or unexpected changes or deviations, in theperformance of a user device or a network infrastructure element thatdeviates from a historical performance pattern or trend. In variousembodiments, the detected conditions may include localized problems,device or core network problems, and device type problems. The localizedproblems may be problems that occur for specific users only at specificlocations. Such localized problems may include a lack of a networksignal or a lack of Wi-Fi calling, which may be detected based on lowsignal strength reported and lack of the Wi-Fi calling activation.Another localized problem be a signal dominance problem that is detectedbased on reports of low signal strength in a particular area, or asignal interference problem that is detected based on normal signalstrength combined with poor signal-to-noise or noise-to-interferenceratio. Congestion is a localized problem that may be detected based onnetwork traffic reports from serving nodes, e.g., eNodeB nodes, in anarea. The detection of a localized problem in the form of anout-of-service node may be performed based on network availabilityreports from serving nodes in an area. Alarm in a serving node is alocalized problem that may be detected based on network alarm reportsfrom serving nodes in an area. The device or core network problems mayinclude a problem in which the radio performance of the user devicewithin a normal performance range but a user is nevertheless havingproblems with the user device. A device type problem may be detectedwhen the relative mean number of issues for a particular device model oroperating system (OS) version, as normalized to the number of devicetypes, is higher than other device models or device version by apredetermined amount or percentage.

The issue detection module 226 may generate alerts at the subscriber,the user device, and/or the network infrastructure element level. Forexample, the user alerts may include “unexpected voice qualitydegradation”, “much lower coverage than average user”, “Wi-Fi callingoff and bad performance at home”, “phone freeze”, etc. The networkalerts may include “poor voice performance with low traffic”, “abnormalamount of handover failures”, etc. The user device alerts may include“increased failures after software upgrade”, “abnormal voice failuresdetected”, “poor LTE radio reception detected”, etc. In someembodiments, the alerts may provide or implement solutions for remedyingfaults. For example, when there is poor coverage, the corresponding userdevice alert may recommend that Wi-Fi calling be enabled on the userdevice. In another example, when there is an abnormal amount of handoverfailures, the corresponding network alert may trigger a modification ofthe handover rule that is used by base stations in a particulargeographical area.

The detection rules and the alert rules that are used by the issuedetection module 226 may be updated in order to detect new conditions orgenerate new types of alerts. The updated detection rules may includedetection rules that have been modified in accordance with userfeedback. In at least one embodiment, the issue detection module 226 maycause the user interface module 228 to generate a user interface thatenables users to rate the usefulness of a detected condition using anumerical rating scale, a positive/negative rating scale, user comments,and/or so forth. In turn, an operator of the communication analyticsengine 124 may edit or modify one or more corresponding detection rulesthat detected the condition to generate updated detection rules.Likewise, the updated alert rules may include rules that have beenmodified in accordance with user feedback. In at least one embodiment,the issue detection module 226 may cause the user interface module 228to generate a user interface that enables users to rate theeffectiveness of an alert using a numerical rating scale, apositive/negative rating scale, user comments, and/or so forth. In turn,an operator of the communication analytics engine 124 may edit or modifyone or more corresponding alert rules to generate the updated alertrules.

The user interface module 228 may generate multiple analytic views thatpresent the KPIs and related analytics data as produced by the KPIcomputation module 224. In various embodiments, the views may include asummary view, a subscriber aggregate view, a subscriber call view, auser device aggregate view, and a top offender view. The summary viewmay include a topology tree that specific time frames of the call data.For example, when a node of the topology tree is selected, the otherinformation that is presented in the summary view may be updated. Thesummary view may further include a KPI selection control, a network mapwindow, a time chart window, and a global performance control. The KPIselection control may be used to select specific KPIs that are to beshown in the network map window and the time chart window. The networkmap window may show VoLTE events clustered by geographic areas. The timechart window may show the historical value for each selected KPI.

The global performance window may show a series of performance KPIs thatare aggregated at the selected node level, for example, for the last 24hours. Such KPIs may include total calls in a time period, thepercentage of circuit-switched (CS) voice calls in the time period, thepercentage of VoLTE calls for the time period, the percentage of Wi-Ficalls for the time period, etc. The global performance window mayfurther show a dropped call rate (DCR) for the time period, averagefailure rate (AFR) for the time period, call setup time (CST) during thetime period, audio quality mean opinion score (MOS) for the time period,handover failure rate for the time period, and VoLTE coverage in apredetermined geographical area for the time period. In variousembodiments, the MOS may be computed based on metrics reported by one ormore user devices, in which the metrics may include measurements such asRTCP, delay, jitter, packet loss, and/or so forth.

In some embodiments, the summary view may further display a chart thatshows voice quality ratings, such as good quality, medium quality, poorquality, or failed. Additionally, the summary view may also display abreakdown of voice problems, such as dropped calls, access failures,one-way audio, repeat calls, etc. The charts may be in the form of piecharts, bar charts, line charts, etc. The summary view may also presenta chart that shows the performance comparison of VoLTE calling vs. Wi-Ficalling, such as comparisons of DCR, AFR, audio quality MOS, CST,coverage for voice over LTE (VoLTE) calling and Wi-Fi calling.

The subscriber aggregate view may present an aggregated view of mobilecalling performance KPIs for a subscriber over a period of time. Thesubscriber aggregate view may include a mobile search box, aninformation panel, a time selection window, an aggregated CS call voicestatistics window, an aggregated VoLTE statistics window, an aggregateWi-Fi calling statistics window, a subscriber alerts window, and a viewcall control. The mobile search box may enable a user to search forinformation on a user device according to International MobileSubscriber Identity (IMSI) or Mobile Station International SubscriberDirectory Number (MSISDN). The information panel may present informationon a user device. The information may include a mobile device number,the mobile device make and model, the software version, whether Wi-Ficalling is on or off, whether VoLTE is on or off, whether Wi-Fi callingis provisioned, whether VoLTE is provisioned, and/or so forth. The timeselection window may enable a user to select a time interval for whichKPIs by calculated, in which the default time interval may be one week.The aggregated CS call voice statistics window may show a percentage oftime the user device engaged in CS calling, the DCR for the calls, theAFR for the calls, the audio quality MOS for the calls, the CST for thecalls, the coverage for the calls, etc. Likewise, the aggregated VoLTEstatistics window may show a percentage of time the user device engagedin voice over LTE (VoLTE) calling, the DCR for the calls, the AFR forthe calls, the audio quality MOS for the calls, the CST for the calls,the coverage for the calls, etc. Additionally, the aggregated VoLTEstatistics window may show a percentage of time the user device engagedin Wi-Fi calling, the DCR for the calls, the AFR for the calls, theaudio quality MOS for the calls, the CST for the calls, the coverage forthe calls, etc.

The subscriber alerts window may show a list of detected alerts for theuser device during the time interval. The view calls control may openthe subscriber call view for the subscriber associated with the selecteduser device. The subscriber call view may present detailed informationabout each call record for a particular subscriber of the wirelesscarrier network 102. Specifically, the subscriber call view may presenta list of call records for a subscriber. Each call record may include asummary that includes a call time, a type of call (e.g., originating,terminating), a technology used by the call (e.g., CS, VoLTE, Wi-Ficalling), a duration of the call, a call status (e.g., call success,call drop, call access fail, and/or so forth). The subscriber call viewmay further include a KPI selection control. A user may use the KPIselection control to select a KPI that may be shown in a geographicalmap and/or as a chart. In some instances, the geographical map may showsubscriber events.

The subscriber call view may further present a performance summarywindow for a selected call and a user device configuration window. Thesummary for the call presented in the performance summary window mayinclude a call time, a type of call (e.g., originating, terminating), atechnology used by the call (e.g., CS, VoLTE, Wi-Fi calling), a durationof the call, a call status (e.g., call success, call drop, call accessfail, and/or so forth), an establishment time, an audio quality MOS, andcoverage details for the call. The user device configuration window mayprovide configuration details for the user device, which may include amake of the user device, a model of the user device, and a softwareversion of the user device, and/or so forth. Additionally, thesubscriber call view may also present a time chart, an alert log, and acall log. The time chart may show historical values of selected KPIs.For example, handover events for the call may be displayed as dots inthe chart. The alert log may list the detected alerts for the call, andthe call log may list all the records for the call.

The user device aggregation view may show aggregated KPI statistics fora particular user device model. The user device aggregation view mayinclude an OS version filter control, a model selection control, anaggregated user device statistics window, a top offender chart, and auser device alert window. The OS version filter control may enable thecustomization of the user device aggregation view to show aggregatedstatistics for specific OS versions or an aggregate of all OS versions.The model selection control may enable the customization of the userdevice aggregation view to show aggregated statistics for one or moreuser device models that are selected from a list of all user devicemodels. The aggregated user device statistics window may display thepercentage of call time that the user device is using VoLTE, CS, andWi-Fi calling technology. For example, the percentages may be displayedas bar graphs, line graphs, or other types of graphs in the aggregateuser device statistics window. The aggregate user device statisticswindow may also display the DCR, AFR, audio quality MOS, CST, and/orcoverage details for the one or more selected user devices. The topoffenders chart may presented a list with a predetermined number of userdevices models with the worst performances for a selected KPI. The userdevice alert window may show a list of detected alerts for each worstperforming user device model.

The top offender view may identify subscribers, user devices, and/ornetwork infrastructure elements that have abnormally poor performance.The top offender view may include a topology tree control, a KPIselection control, a subscriber top offender chart, a user device topoffender chart, a network cell top offender chart, and an alert window.The topology tree control may enable a user to select an area of acellular network to be analysis by selecting a corresponding portion ofthe topology tree. The KPI control may enable the selection of a KPI forwhich the top offenders may be calculated. Accordingly, the subscribertop offender chart may show a predetermined number of worst subscribersfor the selected KPI in the selected area. The user device top offenderchart may show a predetermined number of worst user devices models forthe selected KPI in the selected area. The network cell top offenderchart may show a predetermined number of worst network cells for theselected KPI in the selected area. The alerts window may showsubscriber, network, and/or user device alerts for any selected KPI.

In additional embodiments, the data collection platform 118 may supportadditional analytics engines that monitor and analyze the performance ofthe radio access network 106. These analytic engines may include anetwork analytics engine 230, an active testing engine 232, and a crossanalytics engine 234. The computer instructions and algorithms of eachengine may execute in conjunction with the data collection platform 118to perform specific tasks. In various embodiments, the network analyticsengine 230 may collect network performance data 122 on the radio accessnetwork 106 and/or the core network 108 via the data collection platform118. Such network performance data 122 may include RAN OSS counters,VoLTE call traces, SIP trace data, RTCP trace data, and/or so forth. TheRTCP trace data may enable the detection of an abnormal transmission gapin either an uplink direction or a downlink direction that indicates aone-way audio situation.

The active testing engine 232 may interface with a specific set of userdevices that are equipped with testing applications. The testingapplications may automatically generate voice and data communications onthe radio access network 106. The voice and data communications may begenerated on a periodic basis to enable the testing applications toregularly capture network performance data. In turn, the testingapplications on the specific set of user devices may report the capturednetwork performance data to the active testing engine. The capturednetwork test data may include Mean Opinion Scores (MOS) on voicequality, Perceptual Objective Listening Quality Assessment (POLQA)measurements of speech quality, radio conditions of the base stations,Internet Protocol (IP) connection states, and/or so forth.

The cross analytics engine 234 may cross analyze the data captured bythe communication analytics engine 124, the network analytics engine230, and the active testing engine 232 to develop a comprehensive viewon the performance of the radio access network 106. For example, thecross analytics engine 234 may develop a machine learning model formeasuring Quality of Experience (QoE) based on data collected by thenetwork analytics engine and the active testing engine. In turn, themachine learning model may be used to extrapolate QoE measurements(KPIs) for user devices that are not equipped with VoLTE performancedata collection applications or device testing applications. In anotherexample, the cross analytics engine 234 may correlate the data collectedby the multiple engines to eliminate data anomalies or measurementoutliers that may skew or distort QoE measurements for a set of userdevices. Accordingly, the cross analysis of the device performance data,the network performance data, and the test performance data collected bythe multiple engines may enable the generation of more comprehensivenetwork performance measurements and statistics.

Example User Interface

FIG. 3 is an illustrative user interface 300 that provides keyperformance indicators information and other call related analyticinformation for display in response to user request inputs. The userinterface 300 may be used to perform root cause analysis with respect tocall data for a particular time period within a specific geographicalarea. The user interface 300 may include an overview control 302, anetwork offender control 304, a subscriber offender control 306, and analerts control 308. The overview control 302 may be activated to show amain view of call data for an area of interest that can be analyzed bythe communication analytics engine 124. In various embodiments, the mainview may include a map 310 of a geographical area that corresponds tothe call data being displayed. The map 310 may show representations ofuser devices that are being used by the subscribers of the wirelesscarrier network 102. For example, the map 310 may show a representation312, in which a user selection of the representation 312 may cause theuser interface 300 to display additional device information of the userdevice or KPI that are calculated for the user device. The map 310 mayalso show events that are being monitored by the communication analyticsengine 124. For example, the map 310 may show a network or device event314 that is experienced by a group of user devices, such as droppedcalls, low voice call quality, one-way audio, and/or so forth.

The user interface 300 may further include a KPI category control 316and a KPI control 318. The KPI category control 316 may be used toselect a category of KPIs to be calculated for the call data of theparticular time period within the specific geographical area.Accordingly, the KPI category control 316 may be a drop down menu thatshows categories of KPIs, such as KPIs for circuit-switch calls, VoLTEcalls, or Wi-Fi calls. Once a category of KPIs is selected, the KPIcontrol 318 may be activated to generate a drop down menu of KPIs thatmay be calculated. For example, the KPIs may include a DCR for a timeperiod, an AFR for the time period, a CST, an audio quality MOS for thetime period, and/or so forth, with respect to a selected call type inthe specific geographical area. In some embodiments, the user interface300 may have a filter control 320 that is used to select the type ofanalytic results for display on the map 310, such as KPIs, detectedissues, and alerts.

The network offender control 304 may be activated to display a networkoffender view of one or more network infrastructure components, in whichthe number of abnormal operating conditions or failures caused by eachof the network infrastructure component is above a predeterminedthreshold. Likewise, the subscriber offender control 306 may beactivated to display a subscriber offender view of one or more userdevices, in which the number of abnormal operating conditions orfailures experienced by each user device is above a predeterminedthreshold. The alerts control 308 may be activated to display alertsthat have been issued by the issue detection module 226 for networkinfrastructure components, user devices, user device models, and/or soforth. In additional embodiments, the user interface 300 may displayother views that are generated by the communication analytics engine124.

Example Processes

FIGS. 4-6 present illustrative processes 400-600 for performingcommunication data analysis and reporting. Each of the processes 400-600is illustrated as a collection of blocks in a logical flow chart, whichrepresents a sequence of operations that can be implemented in hardware,software, or a combination thereof. In the context of software, theblocks represent computer-executable instructions that, when executed byone or more processors, perform the recited operations. Generally,computer-executable instructions may include routines, programs,objects, components, data structures, and the like that performparticular functions or implement particular abstract data types. Theorder in which the operations are described is not intended to beconstrued as a limitation, and any number of the described blocks can becombined in any order and/or in mirror to implement the process. Fordiscussion purposes, the processes 400-600 are described with referenceto the architecture 100 of FIG. 1.

FIG. 4 is a flow diagram of an example process 400 for implementing acommunication analytics engine to collect and analyze call data of awireless carrier network. At block 402, the data collection platform 118may establish a communication connection with the wireless carriernetwork 102. In various embodiments, the communication connection may beestablished via the Internet 112. The communication connection mayenable the data collection platform 118 to employ FTP, HypertextTransfer Protocol (HTTP) posts, direct network infrastructure elementconnection, and/or other data transfer techniques to obtain the calldata 116.

At block 404, the data collection platform 118 may collect the call data116 of the user devices 104(1)-104(N) that are using the communicationservices provided by the wireless carrier network 102. The call data 116may be generated by the device applications 114(1)-114(N) that are onthe user devices 104(1)-104(N) as the device application monitor callsplaced and received at the user devices 104(1)-104(N). In someembodiments, the calls may be in the form of VoLTE calls. The deviceapplications 114(1)-114(N) may send the call data 116 to the corenetwork 108 of the wireless carrier network 102. Accordingly, the calldata 116 may be collected by the data collection platform 118 from thewireless carrier network 102 via the communication connection.

At block 406, the data collection platform 118 may convert the call data116 into a format that is readable by the communication analytics engine124 that executes in conjunction with the data collection platform 118.In various embodiments, the call data 116 that is collected for aparticular time period may be in the form of input data files, i.e.,JSON records. The data processing module 216 may merge call data asembodied in the JSON records into an integrated data file. Subsequently,the data processing module 216 may convert the information in theintegrated data file into a converted format that is readable to thecommunication analytics engine 124.

At block 408, the communication analytics engine 124 may aggregate thecall data 116 into data sets according to at least one groupingparameter. In various embodiments, the grouping parameters may include atime period, routing network component, user device manufacturer, userdevice model, and/or so forth.

At block 410, the communication analytics engine 124 may analyze thecall data in a dataset. The analysis may include the calculation of oneor more KPIs with respect to a usage of the wireless carrier network 102by subscribers, user devices, and network infrastructure elements. Insome instances, one or more KPIs may indicate an issue with a usage ofthe wireless carrier network 102 by one or more subscribers, an issuewith one or more user devices, or an issue with one or more networkinfrastructure elements, and/so forth. Accordingly, the communicationanalytics engine 124 may generate one or more alerts regarding suchissues.

At block 412, the communication analytics engine 124 may provideanalytic result for the call data in the dataset for display on acomputing device or a user device. In some embodiments, the analyticsresult may be in the form of one or more analytics reports that areprovided for presentation on the computing device in response to viewrequests. The analytics may be provided to the computing device via anAPI of the communication analytics engine 124. For example, the one ormore views may include a summary view, a subscriber aggregate view, asubscriber call view, a user device aggregate view, and/or a topoffender view. In other embodiments, the analytic result may be in theform of one or more alerts that are provided for presentation on thecomputing device or an affected user device.

At decision block 414, the communication analytics engine 124 maydetermine whether the call data in another dataset is to be analyzed. Invarious embodiments, the communication analytics engine 124 may make thedetermination based on stored configuration settings or a manuallyinputted analysis request. Accordingly, if the communication analyticsengine 124 determines that the call data in another dataset is to beanalyzed (“yes” at decision block 414), the process 400 may loop back toblock 410 so that additional dataset analysis may be performed. However,if the communication analytics engine 124 determines that no furtherdatasets are to be analyzed (“no” at decision block 414), the process400 may loop back to block 404, such that the data collection platform118 may collect additional call data 116 from the user devices104(1)-104(N).

FIG. 5 is a flow diagram of an example process 500 for detecting issueswith respect to Wireless communication and generating alerts regardingthe issues. The process 400 may further illustrate block 410 of theprocess 400. At block 502, the communication analytics engine 124 maygenerate one or more KPIs for the call data in a data set. For example,the KPIs may include a dropped call rate (DCR) for a time period, anaverage failure rate (AFR) for the time period, a call setup time (CST)during the time period, an audio quality mean opinion score (MOS) forthe time period, handover failure rate for the time period, and coveragefor the time period with respect to one or more subscribers, userdevices, network infrastructure elements, and/or so forth. The KPIs maybe generated based on custom KPI configuration settings, which may bemodified via an updated KPI configuration file to enable the generationof additional or different KPIs.

At block 504, the communication analytics engine 124 may analyze theKPIs according one or more detection rules to detect a call issue. Thecall issue may be a condition that affects one or more user devices ofsubscribers or one or more network infrastructure elements of thewireless carrier network. In various embodiments, the detection rulesmay be modified or updated via a detection configuration file such thatdifferent or additional call issues may be detected.

At block 506, the communication analytics engine 124 may generate one ormore alerts regarding the call issue according to one or more alertrules. For example, a series of KPIs that are generated by thecommunication analytics engine over time may trigger an alert that anabnormal condition is developing with respect to some VoLTE-relatednetwork infrastructure components of a wireless carrier network. Invarious embodiments, the alert rules may be modified or updated via analert rule configuration file such that different or additional alertsconcerning call issues may be initiated.

At block 508, the communication analytics engine 124 may receive updatesto the one or more detection rules or the one or more alert rules. Invarious embodiments, the updates may be developed based on user feedbackregarding the effectiveness of the detection rules in detecting callissues and/or the effectiveness of the alert rules. For example, adetection rule may be refined to be more sensitive to the detection ofnetwork infrastructure failure within the radio access network 106. Inanother example, an alert rule may be refined to provide a faster alertwith respect to the failure of a LTE radio in a user device.Subsequently, the process 500 may loop back to block 504 such thatadditional detections and/or alerts may be performed based on theupdated detection and/or alert rules.

FIG. 6 is a flow diagram of an example process 600 for providing viewsof call analytics data in accordance with user requests. At block 602,the communication analytics engine 124 may determine whether a requestfor a summary view of the call analytics results is received. In variousembodiments, a view request may be initiated at a computing device andtransmitted to the communication analytics engine 124 via an APIprovided by the communication analytics engine 124. Accordingly, atdecision block 604, if the communication analytics engine 124 determinesthat the request for the summary view is received, the process 600 maycontinue to block 606. At block 606, the communication analytics engine124 may provide the summary view of the call analytics results forpresentation. For example, a provide view of the call analytics resultsmay be received and displayed by the computing device.

At block 608, the communication analytics engine 124 may determinewhether a request for a subscriber aggregate view of the call analyticsresult is received. Accordingly, at decision block 610, if thecommunication analytics engine 124 determines that the request for thesubscriber aggregate view is received, the process 600 may proceed toblock 612. At block 612, the communication analytics engine 124 mayprovide the subscriber aggregate view for presentation.

At block 614, the communication analytics engine 124 may determinewhether a request for a subscriber call view of the call analyticsresult is received. Accordingly, at decision block 616, if thecommunication analytics engine 124 determines that the request for thesubscriber call view is received, the process 600 may proceed to block618. At block 618, the communication analytics engine 124 may providethe subscriber call view for presentation.

At block 620, the communication analytics engine 124 may determinewhether a request for a user device aggregate view of the call analyticsresult is received. Accordingly, at decision block 622, if thecommunication analytics engine 124 determines that the request for theuser device aggregate view is received, the process 600 may proceed toblock 624. At block 624, the communication analytics engine 124 mayprovide the user device aggregate view for presentation.

At block 626, the communication analytics engine 124 may determinewhether a request for a top offender view of the call analytics resultis received. Accordingly, at decision block 628, if the communicationanalytics engine 124 determines that the request for the top offenderview is received, the process 600 may proceed to block 630. At block630, the communication analytics engine 124 may provide the top offenderview for presentation.

At block 632, the communication analytics engine 124 may determinewhether a request for another view of the call analytics result isreceived. The additional view may present a perspective on the callanalytics results that is not presented by the other views. Accordingly,at decision block 634, if the communication analytics engine 124determines that the request for the additional view is received, theprocess 600 may proceed to block 636. At block 636, the communicationanalytics engine 124 may provide the additional view for presentation.

Returning to decision block 604, if the communication analytics engine124 determines that the request for the summary view is not received,the process 600 may proceed directly to block 608. Returning to decisionblock 610, if the communication analytics engine 124 determines that therequest for the subscriber aggregate view is not received, the process600 may proceed directly to block 614. Returning to decision block 616,if the communication analytics engine 124 determines that the requestfor the subscriber call view is not received, the process 600 mayproceed directly to block 620. Returning to decision block 622, if thecommunication analytics engine 124 determines that the request for theuser device aggregate view is not received, the process 600 may proceeddirectly to block 626. Returning to decision block 628, if thecommunication analytics engine 124 determines that the request for thetop offender view is not received, the process 600 may proceed directlyto block 632. Returning to decision block 634, if the communicationanalytics engine 124 determines that the request for the additional viewis not received, the process 600 may loop back to block 602.

The implementation of the communication analytics engine to execute inconjunction with the data aggregation and processing platform mayprovide a unified and scalable solution for call data aggregation andprocessing. The data and aggregation and processing platform may enablethe use of custom analytics engines to provide KPIs to a wirelesscarrier network for areas such as network development, operations, andcustomer care. The KPIs that are provided by the communication analyticsengine from the call data may be otherwise overly burdensome orvirtually impossible to manually obtain in a timely manner due to thelarge volume of call data that typically inundate a wireless carriernetwork. Further, the techniques may provide an advanced approach tocustomer care with proactive and individualized treatments of wirelesssubscribers in real time.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. One or more non-transitory computer-readablemedia storing computer-executable instructions that upon execution causeone or more processors to perform acts comprising: establishing acommunication connection between a data collection platform executingone or more computing devices and a wireless carrier network; collectingcall data of multiple user devices at the data collection platform viathe communication connection, the multiple user devices use the wirelesscarrier network to initiate and receive calls to one or more additionaldevices; converting the call data into a format that is readable by acommunication analytics engine that executes in conjunction with thedata collection platform on the one or more computing devices; andanalyzing at least a portion of the call data via the communicationanalytics engine executing on the one or more computing devices togenerate analytic results that includes one or more key performanceindicators (KPIs).
 2. The one or more non-transitory computer-readablemedia of claim 1, wherein the acts further comprise providing theanalytic results for the call data to a computing device forpresentation.
 3. The one or more non-transitory computer-readable mediaof claim 2, wherein the providing the analytics results includesproviding a summary view of the analytic results that includes atopology tree, wherein a node of the topology tree is selectable to showspecific voice over LTE (VoLTE) KPI information for a particular timeperiod, the specific VoLTE KPI information including at least one of adropped call rate (DCR) for the particular time period, an averagefailure rate (AFR) for the particular time period, a call setup time(CST) during the particular time period, an audio quality mean opinionscore (MOS) for the particular time period, a handover failure rate forthe time period, and VoLTE coverage in a predetermined geographical areafor the particular time period.
 4. The one or more non-transitorycomputer-readable media of claim 3, wherein the summary view furtherdisplays at least one of a chart that shows voice quality ratings, abreakdown of voice problems that includes one or more of dropped calls,access failures, or repeat calls, or a chart that shows performancecomparisons of one or more of the DCR, the AFR, the audio quality MOS,the CST for voice over LTE (VoLTE) calling and Wi-Fi calling.
 5. The oneor more non-transitory computer-readable media of claim 2, wherein theproviding the analytics results includes providing a subscriberaggregate view of mobile calling performance KPI information for asubscriber over a time period that shows VoLTE calling and Wi-Fi callingstatistics of the subscriber over the time period.
 6. The one or morenon-transitory computer-readable media of claim 2, wherein the providingthe analytics results includes providing a subscriber call view thatshows KPI information regarding call made by a subscriber that includesone or more of a duration of the call, a call success or failure statusof the call, whether the call is an originating or terminating call,whether the call uses circuit-switched calling, VoLTE calling or Wi-Ficalling, or configuration details of the user device used to make thecall.
 7. The one or more non-transitory computer-readable media of claim2, wherein the providing the analytics results includes presenting auser device aggregate view that shows aggregated KPI statistics for aparticular user device model that includes at least one of a droppedcall rate (DCR), an average failure rate (AFR), an audio quality meanopinion score (MOS), a call setup time (CST), or coverage details forthe user device model.
 8. The one or more non-transitorycomputer-readable media of claim 2, wherein the providing the analyticsresults includes presenting a top offender view that identifies apredetermined number of one or more subscribers, one or more userdevices, or one or more network infrastructure components that have theworst performance for a selected KPI in a selected area.
 9. The one ormore non-transitory computer-readable media of claim 1, wherein the actsfurther comprise: analyzing the one or more KPIs at the communicationanalytics engine according to one or more detection rules to detect acall issue that affects one or more user devices using the wirelesscarrier network or one or more network infrastructure elements of thewireless carrier network; generating one or more alerts regarding thecall issue at the communication analytics engine according to at leastone alert rule; and providing the one or more one or more alertsregarding the call issue to a computing device for presentation.
 10. Theone or more non-transitory computer-readable media of claim 10, whereinthe acts further comprises receiving an update to the one or moredetection rules or the one or more alert rules that are generated basedon user feedback regarding effectiveness of the one or more detectionrules or the one or more alert rules.
 11. The one or more non-transitorycomputer-readable media of claim 1, wherein the collecting includescollecting corresponding call data of a user device that is provided bya device application on the user device that obtains the correspondingcall data via an IP Multimedia Subsystem (IMS) stack trace.
 12. The oneor more non-transitory computer-readable media of claim 1, wherein thecollecting includes collecting the call data via a data management layerof the data collection platform, the data collection platform furtherincluding a cloud layer that store the call data collected by the datamanagement layer in multiple virtual data storage clusters.
 13. The oneor more non-transitory computer-readable media of claim 1, wherein theconverting includes converting the call data included in JavaScriptObject Notation (JSON) records for a particular time period into anintegrated data file that is readable by the communication analyticsengine.
 14. The one or more non-transitory computer-readable media ofclaim 1, further comprising analyzing the call data in conjunction withnetwork performance data collected by a network analytics engine andnetwork test data collected by an active testing engine to extrapolateone or more additional key performance indicators (KPIs).
 15. Acomputer-implemented method, comprising: establishing a communicationconnection between a data collection platform executing one or morecomputing devices and a wireless carrier network; collecting call dataof multiple user devices at the data collection platform via thecommunication connection, the multiple user devices use the wirelesscarrier network to initiate and receive calls to one or more additionaldevices; converting the call data into a format that is readable by acommunication analytics engine that executes in conjunction with thedata collection platform on the one or more computing devices;collecting network performance data from the wireless carrier networkvia one or more data adaptors; analyzing at least a portion of the calldata and at least a portion of the network performance data via thecommunication analytics engine executing on the one or more computingdevices to generate analytic results that includes one or more keyperformance indicators (KPIs); and providing the analytic results forthe call data to a computing device for presentation.
 16. Thecomputer-implemented method of claim 15, further comprising: analyzingthe one or more KPIs at the communication analytics engine according toone or more detection rules to detect a call issue that affects one ormore user devices using the wireless carrier network or one or morenetwork infrastructure elements of the wireless carrier network;generating one or more alerts regarding the call issue at thecommunication analytics engine according to at least one alert rule; andproviding the one or more one or more alerts regarding the call issue toa computing device for presentation.
 17. The computer-implemented methodof claim 16, further comprising receiving an update to the one or moredetection rules or the one or more alert rules that are generated basedon user feedback regarding effectiveness of the one or more detectionrules or the one or more alert rules.
 18. The computer-implementedmethod of claim 17, wherein the collecting includes collectingcorresponding call data of a user device that is provided by a deviceapplication on the user device that obtains the corresponding call datavia an IP Multimedia Subsystem (IMS) stack trace.
 19. A system,comprising: one or more processors; and memory including a plurality ofcomputer-executable components that are executable by the one or moreprocessors to perform a plurality of actions, the plurality of actionscomprising: establishing a communication connection between a datacollection platform executed by the one or more processors and awireless carrier network; collecting call data of multiple user devicesat the data collection platform via the communication connection, themultiple user devices use the wireless carrier network to initiate andreceive calls to one or more additional devices; aggregating the calldata into one or more datasets according to at least one groupingparameter; analyzing the call data in a dataset via a communicationanalytics engine executed by the one or more processors to generateanalytic results that includes one or more key performance indicators(KPIs); analyzing the KPIs at the communication analytics engineaccording to one or more detection rules to detect a call issue thataffects one or more user devices using the wireless carrier network orone or more network infrastructure elements of the wireless carriernetwork; and generating one or more alerts regarding the call issue atthe communication analytics engine according to at least one alert rule.20. The system of claim 19, wherein the plurality of actions furthercomprise at least one of providing the analytic results for the calldata to a computing device for presentation or providing the one or moreone or more alerts regarding the call issue to a computing device forpresentation.
 21. The system of claim 19, wherein the plurality ofactions further comprise receiving an update to the one or moredetection rules or the one or more alert rules that are generated basedon user feedback regarding effectiveness of the one or more detectionrules or the one or more alert rules.